_UpperCAmelCase          =						"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich







import numpy as np
from PIL import Image




def 							UpperCamelCase		(			__lowercase		:					np.ndarray		,__lowercase		:					int		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							List[Any]      = np.array(__lowercase      )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix'      )
		A_						:							str      = 0
		A_						:							Any      = 0
		A_						:							Optional[int]      = 0
		A_						:							Optional[Any]      = 0

		# compute the shape of the output matrix
		A_						:							str      = (arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		A_						:							str      = np.zeros((maxpool_shape, maxpool_shape)      )

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						A_						:							Any      = np.max(arr[i : i + size, j : j + size]      )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				A_						:							Tuple      = 0
				A_						:							Optional[int]      = 0

		return updated_arr




def 							UpperCamelCase		(			__lowercase		:					np.ndarray		,__lowercase		:					int		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							int      = np.array(__lowercase      )
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix'      )
		A_						:							Any      = 0
		A_						:							Optional[int]      = 0
		A_						:							List[str]      = 0
		A_						:							Union[str, Any]      = 0

		# compute the shape of the output matrix
		A_						:							int      = (arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		A_						:							int      = np.zeros((avgpool_shape, avgpool_shape)      )

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						A_						:							str      = int(np.average(arr[i : i + size, j : j + size]      )      )
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				A_						:							List[Any]      = 0
				A_						:							int      = 0

		return updated_arr


# Main Function
if __name__ == "__main__":
	from doctest import testmod

	testmod(name="""avgpooling""", verbose=True)

	# Loading the image
	_UpperCAmelCase          =						Image.open("""path_to_image""")

	# Converting the image to numpy array and maxpooling, displaying the result
	# Ensure that the image is a square matrix

	Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

	# Converting the image to numpy array and averagepooling, displaying the result
	# Ensure that the image is a square matrix

	Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()






